{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import irispy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = irispy.Polyhedron.fromBounds([0.1, 0.2], [0.95, 0.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<irispy.iris_wrapper.Polyhedron at 0x10cb5a900>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.patches.Polygon at 0x10eebbb50>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Rbt8PIN3S9w1XA76j1nmrZEAwNoa6yTF1trSJ/vF11275PLPjnMfAMDa8V0MAEBHIAAA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\ngQAAdAQCANARCABA5/8CL3ujnJ/lYWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eebb910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 0.,  1.],\n",
       "       [-1., -0.],\n",
       "       [-0., -1.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.getA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
